Will Data Analytics Allow Us to “Do Less Law?”

Hardly a day goes by in the legal market without an article or blog post about alternative fee arrangements (AFA) or delivering more “value”. Yet both clients and law firms struggle to define value and adopt alternatives to the billable hour. Perhaps the time has come to re-think the question.

Changing the Focus to Preventive Law

“More value” and AFA means reducing legal costs, not just quoting fees as an hours estimate times the rate. A real AFA therefore requires steps such as process improvement, automation, and alternative staffing. To be sure, these reduce cost and improve how lawyers practice. But are we missing a more basic, first step? Perhaps we can reduce legal spend by “doing less law”. Avoiding an activity altogether saves more than simply improving its efficiency. I raised this possibility in my November 2011 blog post, To Reduce Legal Spend, Do Less Law. Clients have two promising paths to doing less law:

First, decide how much legal effort – if any – a problem warrants. Doing so means making tough decisions; it means assessing potential outcomes and making risk-adjusted decisions. It will be an anathema to many. Before you object to the possibility, however, consider how you would demonstrate that the level of legal investment today is economically s. If we can develop an analytically sound framework – one focused more on outcomes than inputs – then we may be able to allocate legal resources more sensibly. Clients may find they are doing too much law, too little, or, if luck strikes, just the right amount. This problem is, alas, so hard that I will leave it to others to tackle.

Second, avoid legal problems. Occasional articles and talks address preventive law. I have not seen, however, sustained and systematic effort by clients – or their outside counsel – to detect and prevent legal problems before they occur. We may find that the former costs more than the latter but without a better try at prevention, we are simply flying blindly.

The Promise and Limitations of Data Analytics and Modeling to Support Preventive Law

A recently published article spurred my thinking on one potential approach to preventive law: analyzing corporate data to identify, in advance, where legal problems might arise. If we can predict problems, then we at least have a chance to avoid them.

I will discuss here a recently published article, disagree with the authors’ conclusions, and offer some ideas for data collection and analysis that might allow us to avoid some legal costs. In What Computer Models Can – and Can’t – Do (Corporate Counsel, January 2013), authors Ryan McConnell (Baker & McKenzie partner), Dianne Ralston (Schlumberger Ltd. deputy GC), and Charlotte Simon (Baker & McKenzie associate) discuss what Nate Silver’s recent book, The Signal and the Noise—Why So Many Predictions Fail But Some Don’t, means for lawyers. Though their article focuses on the U.S. Foreign Corrupt Practices Act, the principles apply broadly across practices.

Noting that Silver’s book “discusses sorting through empirical data and identifying signals that enable better decision making”, the authors ask “can techniques developed in fields as diverse as weather forecasting and baseball management be applied to developing a risk-based compliance program?” More specifically, they ask “how do compliance lawyers sort through the noise to create a valuable risk-based program?” To answer this, they examine several data sources that might support compliance and point out the “noise” problems with each:

Lawyers could focus compliance training based on corporate job descriptions but the authors express concern about the accuracy of job descriptions and determining which pose the most risk.

“[C]alls to a compliance department’s ethics hotline highlights other sampling issues”, specifically that there may not be enough historic data or the calls may exclude some regions so you “will only get half the story.”

A gift tracking system might highlight questionable spending but can never tell you the expense was “improper entertainment because it cannot reveal the intent”.

The “new FCPA guidance notes that taking a risk-based approach is particularly critical with respect to due diligence procedures for assessing third-party relationships” but the authors see constraints in the “reliability of the data obtained from the third party to assess risk and, for initial due diligence purposes”.

The authors conclude “The success of a risk-based model, however, will ultimately depend not on technology tools, but on the compliance lawyer’s ability to successfully analyze risk data and sort the signals from the noise. That lawyer must be adaptive, creative, and look beyond the data to see organizational and industry trends and risks. By helping us understand the limits of technology and how to use data, Nate Silver can make us all better compliance lawyers.”

So Modeling is Tough – That Means We Need to Try Harder, Not Stop

Reading the article, we might conclude that lawyers should seek less data, not more. That would be discouraging, and I think wrong. I think a better conclusion is “get more and better data and improve the hypotheses and analyses”. Find ways to reduce the noise!

In my view, lawyers should work with corporate colleagues to identify additional existing data repositories or collect additional data and then to improve the models. We do not lack for tools that can help: a host of both legacy and newer “Big Data” software support many types of models. Rather, the limitations lie in data quality and quantity, creativity and rigor in modeling, and validating model outputs.

Models may have limits but we must consider the alternatives to deploying them more systematically. The alternative is simple but painful: more lawyers and more staff. With compliance costs skyrocketing, can companies simply continue to hire more and more professionals? Even with more resources, we may mist serious compliance problems.

The “noise” concern here reminds me of the debate around predictive coding in e-discovery. Computerized document review has limits, but so does human review. Models and data have limits, but so too do humans.

Data modeling applies to most practices. This means that lawyers thus must grapple with the issues raised here. To do so, they must overcome a fear of numbers and seek opportunities to work with statisticians and analysts who can support this work. With more efforts at serious modeling, we might conclude it really does not work. I have seen no evidence that we are anywhere near that point.

Data Collection and Modeling Ideas for More and Better Preventive Law

So, to spur thinking about where data collection and modeling might help avoid legal problems and support compliance, I offer here a few ideas to consider:

Aggregate Data across Companies. The authors discuss using job descriptions to aid in compliance but conclude there is too much noise in that data. More data often solves noise problems, so why not aggregate job descriptions across companies – that could yield more insight into problematic positions or locations than any one company’s data. Thinking about ‘compliance as a utility’, there may be multiple opportunities for companies to share non-competitive data to improve compliance. Large data sets, as the authors observe, usually yield more reliable results.

Analyze E-mail Communication Patterns. Companies have a very rich, extant store of data that may well yield compliance clues: e-mail messages, files, and databases. Subject to privacy and other potential legal limits, companies could analyze the e-mail headers to look for suspicious patterns of communication. Suspicious might include too much, too little, or unusual combinations of people in touch. Start by finding a known compliance problem and do this analysis retroactively to learn what analysis might be predictive.

Analyze E-mail Content. Going one step further with e-mail, companies could perform semantic analysis on e-mail content (not just headers) to look for suspicious substantive discussions. Already in the 1990s the US financial sector did this (using, for example, Assentor), to identify broker e-mail messages that violated securities rules. Today, with the predictive coding techniques developed for e-discovery, much more is possible – and affordable.

Tap Corporate Databases. Corporate data does not stop with e-mail. Databases to support operations, sales, and expense management may also yield pointers for where to look for compliance issues. With social media, the possibilities seem endless.

Collect Original Data. If the data the authors discuss, and if e-mail and corporate records do not suffice, then collect data. Compliance officers could consider web-based surveys. If that loses too much nuance, then they could deploy a team of low cost lawyers to make outbound calls to interview selected employees and systematically enter the interview results into a database for analysis. Who said we have to stop with off-the-shelf data?

Try Triage with Models and More Junior Lawyers. Models may never be 100% reliable but they may be sufficiently reliable for triage. If a model can bucket outcomes into ‘almost certainly not a problem’, ‘almost certainly a problem, and ‘may be a problem’, then lawyers at least have some indication of where to look. A team of more junior lawyers, perhaps offshore or in a low-cost domestic location, could apply human judgment to refine model results and surface the most suspicious findings to in-house counsel.

Moving Forward: A Call for Research & Development

Which of the ideas above work and which do not is almost a less interesting question than how we arrived in the 21st Century without much of a clue about the answer – or even how to think about the question. Am I the only one who thinks it odd – and wrong – that large corporate law and compliance departments seemingly conduct little or no research and development? Companies that employ hundreds of lawyers and compliance professional already spend a lot on law. Some companies’ legal budgets are in the hundreds of millions dollar or pound range. At that scale, why not do some R&D to find ways to reduce cost? Granted, that R&D might yield poor results. Without trying, however, how do we know? Perhaps the research would lead to much lower ongoing legal or compliance costs.

This is not even another cry for Big Data. All these ideas can be tested with tools that have been available for years. The floor is open for other ideas, Big Data or otherwise, and to start the discussion about why we do not see more R&D to reduce legal spend.

Editor's note - this article was republished with the permission of the author, and appeared on his blog.